Electrospun polyacrylonitrile (PAN) nanofibers are widely used as precursors for carbon nanofibers, but their conversion typically relies on time- and energy-intensive high-temperature processes. Here, we demonstrate that rapid thermal processing (RTP) enables the formation of electrically functional carbon nanofiber networks within seconds. Following conventional oxidative stabilization, PAN nanofiber mats were treated under nitrogen by RTP at 800-1000°C with dwell times between 5 to 120 seconds. Electrical measurements reveal a sharp decrease in sheet resistance with increasing RTP temperature, reaching a minimum value of 29 Ω sq−1 after 120 s at 1000°C. Structural and spectroscopic analyses indicate the rapid formation of electrically conductivesp2-rich carbon, while longer dwell times lead to progressive compositional and structural refinement. These results demonstrate that, once stabilized, PAN nanofibers can be converted into electrically conductive carbon on timescales orders of magnitude shorter than conventional processes, establishing RTP as an effective route for the ultrafast fabrication of functional carbon nanofibers.
The development of carbon alloy ORR catalysts requires practical methods for their characterization and comparison. In this study, two carbon alloy ORR catalysts (CA and CA-Fe), together with a Pt/C reference, were characterized using rotating ring–disk electrode (RRDE) measurements over a range of catalyst loadings. The conventional RRDE metrics, the half-wave potential (E1/2) and hydrogen peroxide yield (fH2O2), exhibit different loading dependences. As a result, direct comparison of catalyst characteristics becomes difficult. To address this limitation, we developed a descriptor-based characterization framework. It employs two phenomenological descriptors, kf2 and ks2. They were derived from RRDE linear-sweep voltammetry within a simplified reaction–diffusion framework. The descriptor kf2 represents the overall tendency of H2O2 formation. The second descriptor, ks2, represents the overall disappearance of H2O2 within the catalyst layer as reflected in the RRDE response. Both descriptors showed only small variations with catalyst loading and electrode rotation rate under the present experimental conditions. As a proof of concept, CA, CA-Fe, and Pt/C were mapped onto this descriptor space. Each catalyst occupied a distinct position. These results demonstrate that the framework provides a common basis for characterizing and comparing the catalyst systems examined in this study.
Exploring new hybrid carbon materials with tunable properties remains critical for expanding the applications of carbon allotropes. In this study, molecular dynamics (MD) simulation is utilized to model the pyrolysis of three distinct polymer precursors, i.e., epoxy (SU-8), acrylic (PMMA), and thermoplastic (PEI), to understand how precursor structure influences final carbonaceous nanostructures. MD modeling predicted that SU-8 yields turbostratic, 3D amorphous structures, while PMMA produces highly ordered, graphene-like 6-membered carbon rings. PEI simulations revealed a mixture of flat sp2 6-membered rings and nitrogen radical-rich amorphous domains. Simulating precursor mixtures successfully generated hybrid materials with enhanced graphene-like behavior and tunable graphitization. These computational predictions were validated experimentally using Raman, XRD, XPS, SEM, HRTEM, and EELS. PMMA and PMMA-SU-8 hybrids exhibited significantly lower Raman ID peaks, signaling superior graphitic character. HRTEM along with Raman, XRD, XPS, and EELS confirmed ordered, flat graphene-like domains in PMMA-derived carbon (interlayer spacing: 3.4–3.6 Å) compared to the disordered, amorphous morphology of SU-8 (3.6–4.0 Å). Furthermore, EELS demonstrated ∼20% higher sp2 content and stronger π - π* transitions in PMMA than in SU-8. This predictive MD-guided framework offers a robust pathway for designing advanced hybrid carbon materials with tailored graphitization and nanostructural dimensions.
Water management remains a critical challenge in polymer electrolyte fuel cells (PEFCs), as excessive liquid water accumulation can obstruct reactant transport, destabilize cell voltage, and reduce performance. In this work, the established liquid bridging principle of interdigitated electrodes is implemented using directly laser-written, uncoated laser induced graphene (LIG) electrodes for over-humidification detection under PEFC-relevant conditions. The resulting LIG-based moisture analyser (LIGMA) is fabricated by CO2 laser patterning of polyimide without masks or additional humidity sensitive coatings. Raman spectroscopy confirms graphitic carbon formation, while scanning electron microscopy reveals the characteristic porous and fibrous LIG morphology. Different IDE geometries are evaluated to determine the influence of finger spacing and active area on droplet response. The sensors show response times of 1-4 s, and the fine geometry reliably detects the smallest investigated droplet volume of 2 µl under the defined deposition protocol. This value is reported as the minimum experimentally tested and detected volume. The voltage response is interpreted as a coupled process involving liquid water bridging, capillary spreading and retention within the porous LIG network, and possible interfacial effects. During 100 h exposure to humidified hydrogen and air, the sensors remained electrically functional and showed no visible delamination, cracking, or pore collapse in the examined regions. Initial testing at the cathode outlet of an operating PEFC shows pronounced signal changes during extended outlet wetting. The results demonstrate the potential of directly patterned LIG IDEs as simple outlet side indicators for condensation and liquid water breakthrough in PEFC systems.
The development of synthesis strategies capable of controlling the morphology and surface properties of metal oxide nanostructures remains essential for expanding their technological and biomedical applications. Two-dimensional carbon-based materials, such as graphene oxide (GO), have been explored as structural templates due to their oxygenated functional groups and ability to direct heterogeneous nucleation. However, the cytotoxicity and biocompatibility of nanomaterials are governed by multiple interconnected factors, including specific surface area, nanosheet thickness, morphology, surface chemistry, aggregation state, synthesis route, and residual species. In this work, GO synthesized by a modified Hummers’ method was used as a template for the preparation of Nb₂O₅ nanosheets and compared with Nb₂O₅ obtained by a microwave-assisted solvothermal route without a template. The samples were evaluated using XRD, SEM, TEM, EDS, XPS, UV–Vis spectroscopy, BET analysis, and MTT cytotoxicity assays. The GO-templated route promoted the formation of more homogeneous, ultrathin, and structurally organized Nb₂O₅ nanosheets, with a higher specific surface area of 115.7 m²/g compared with 55.8 m²/g for the non-templated sample. XPS confirmed the predominance of Nb⁵⁺ in both materials, while slight differences in the Nb 3d region suggest changes in the surface chemical environment induced by the GO template. Despite the higher surface area, both Nb₂O₅ samples maintained cell viability above 70% under the tested concentrations and exposure times. This behavior suggests that the biological response was not governed by surface area alone, but by the combined influence of nanosheet thickness, morphology, aggregation state, crystallinity, and surface chemistry. These results indicate that GO-assisted synthesis improves the morphological and textural properties of Nb₂O₅ nanosheets while maintaining an acceptable in vitro cytocompatibility profile.
A novel and efficient magnetic activated carbon/cobalt nanocomposite (MAC) was, for the first time, prepared as a new eco-friendly and cost-effective adsorbent. Using a variety of characterization techniques, its morphological, chemical, and surface properties were investigated. The MAC can be easily separated from aqueous solutions using an external magnet and, thus, is a promising adsorbent for the removal of p-nitrophenol (PNP) from wastewater. The removal of PNP was studied under different operating conditions, including pH, ionic strength, contact time, temperature, and PNP concentration. To evaluate the adsorption equilibrium, Langmuir, Freundlich, and Temkin isotherm models were applied. The higher R² value obtained for the Langmuir model indicated that the adsorption process was best described by this model.Kinetic models, including the intraparticle diffusion model, Lagergren’s pseudo-first and pseudo-second-order models, as well as the Elovich model were used to analyze the kinetics of PNP adsorption onto MAC, with the Elovich model providing the best fit and a predicted adsorption capacity of 35.57 mg g⁻¹. The adsorbed PNP was effectively desorbed using ethanol, and the MAC nanocomposite retained its adsorption performance for more than six adsorption–desorption cycles. According to the findings, the prepared nanocomposite is an efficient adsorbent for the large-scale removal of PNP from wastewater through adsorption processes.
This review discusses different types of carbon dots and synthesis methods, including hydrothermal, microwave, pyrolysis, electrochemical, and ultrasonic techniques. It highlights green synthesis using plant and biomass materials as eco-friendly approaches. CDs are characterized using techniques like UV-Vis, FTIR, XRD, TEM, SEM, XPS, photoluminescence and zeta potential. Recent applications in drug delivery, bioimaging, biosensing, heavy metal detection, environmental remediation, agriculture, food safety, photocatalysis and energy-related fields are comprehensively reviewed. However, challenges such as limited understanding of fluorescence, large-scale production, and clinical use. Future research should focus on controlled synthesis, red/NIR CDs, and sustainable production. Overall, CDs have strong potential for future applications.
This study presents an experimentally informed geometrical reconstruction framework for three-dimensional carbon black (CB) aggregates used as reinforcing fillers in rubber compounds. The framework combines diffusion-limited aggregation concepts with fractal scaling and grade-specific experimental descriptors, including primary particle size, aggregate size, fractal characteristics, overlap coefficients, and specific surface area. The generated aggregate populations are evaluated at the population level against grade-specific SBET reference ranges. The morphology of selected aggregates is compared with relevant TEM-images from literature. The proposed approach is intended as a descriptive reconstruction method for generating geometry-resolved CB aggregate populations suitable for subsequent numerical simulations. The results show that the simulated aggregate populations reproduce standardised surface-area descriptors across the studied CB grades while preserving stochastic intra-grade variability in aggregate morphology. Furthermore, the morphologies of the generated aggregates compare well with reported literature images.
Since chemistry is primarily responsible for the covalent bonds in C12 carbyne ring and oxygen molecules, physics through electrostatic interactions / or forces governs the reversibility behavior, enabling rechargeable batteries with lithium oxide ions (Li2O) as an energy vector. We constructed a periodic system with boundary conditions of four C12 carbyne rings and 5 oxygen O2 molecules in a 10 × 10 × 10 ų simulation box. We performed classical molecular dynamics (MD) simulations by using the NVE microcanonical ensemble to stabilize the system. During the MD simulations the system reached a stable configuration in which the oxygen molecules were replaced by Li2O (lithium oxide ions). Annealing was first performed, followed by simulations in the microcanonical NVE ensemble to stabilize the system until the kinetic energy stayed near non-bond energy and the potential energy near total energy to satisfy the principle of energy conservation. Then, we applied the NPT ensemble to observe whether the density behaves properly to achieve stability. Particular attention was given to the formation of dimers in face-to-face and T-shaped conformations on the carbyne rings used. Finally, we obtained material exhibiting a specific surface area of 3980.69 [m2/g], a diffusion coefficient of 6.328 × 10–13 [m2/s], and an electrical conductivity of 3.158 × 10–3 [S m-1].
The adsorption capacity of biochar and leonardite for lead removal in water was evaluated under controlled laboratory conditions. The physical and chemical characteristics of both materials were analyzed, including porosity via Scanning Electron Microscopy (SEM), Total Organic Carbon (TOC) via the Walkley-Black method, and elemental chemical analysis via Energy Dispersive Spectroscopy (EDS). Optimal factors (pH, adsorbent dose, agitation speed, and contact time) were determined by applying an experimental design consisting of 4 treatments with double replication for determining the optimal pH and 9 treatments with triple replication for determining the dose, contact time, and agitation speed. The micrographs demonstrated that biochar exhibits a porous structure with a pore diameter size between 20.21μm and 32.30μm. The surface area of biochar at a particle size of 0.250 mm is 28.85 m²/g and the TOC is 35.295 g/kg. Leonardite did not exhibit porosity, which led to its activation with Calcium Chloride (CaCl₂); its total carbon content was 31.1%. EDS analysis revealed a high total carbon content for both biochar and leonardite. The optimal adsorption pH was 5 for both materials. Since each experimental unit contained 50 mL of solution, the applied adsorbent dosages correspond to 2.0 mg mL⁻¹ (100 mg), 1.5 mg mL⁻¹ (75 mg) and 1.0 mg mL⁻¹ (50 mg) for leonardite. Biochar performed better at 50 rpm and 10 min, whereas leonardite was optimal at 100 rpm and 20 min. Statistical analysis revealed that both dose and agitation speed are significant factors for adsorption. Contact time did not present a significant main effect (p > 0.05); however, a significant three-way interaction among dose, agitation speed and contact time was observed for biochar, indicating that the influence of contact time depends on the simultaneous levels of the remaining operational variables. Leonardite removed 99.19% of the lead, proving to be a highly effective option, while biochar achieved 55.83% removal.
Understanding pyrolysis of phenolic resin is key for predicting the material response of phenol-based thermal protection systems (TPS), such as the Phenolic Impregnated Carbon Ablator (PICA). Experiments provide data at realistic reentry conditions but lack microscopic insight, while Molecular Dynamics (MD) studies provide microscopic insight but only up to a few nanoseconds, typically at unrealistically high temperatures. To address this problem, we present the first Accelerated Molecular Dynamics study of the pyrolysis of a phenolic resin precursor molecule using Parallel Replica Dynamics (PRD). We build and validate a PRD framework applicable to the study of small phenolic resin systems and achieve timescales of several microseconds at temperatures as low as 1600 K, representing a 1000-fold increase in simulation time compared to past MD studies at comparable conditions. Our simulations reveal that phenolic resin undergoes intense competition between carbon dissociation and formation reactions, occasionally entering kinetic traps. These processes ultimately lead to planar graphene-like structures similar to Basic Structural Units (BSU), the fundamental building blocks driving graphitization of bulk phenolic resin. We extract Arrhenius parameters for eight distinct reaction classes, identify the dominant role of hydrogen transfer at lower temperatures, and demonstrate that C–C bond formation becomes increasingly competitive with decomposition below 2000 K. These findings provide a microscopic foundation for developing next-generation pyrolysis models for thermal protection materials.
Efficient production of high-quality carbon nanotubes (CNT) with unique properties together with high hydrogen yield is a challenge for methane pyrolysis. In this study, targeted microwave heating and commercial Ni (65wt%) supported Al2O3-SiO2 catalysis mixed with SiC (1:10) is employed to produce hydrogen and multi-walled CNT (MWCNT) using methane pyrolysis. Different low operating temperatures (700–900 °C) and a fixed methane/nitrogen ratio (1:1) are used to reduce the cost of CNTs production. Microwave heating results in fast heating up on nickel active sites mixed with SiC which prevents carbon deposition on the wall. The MWCNTs grown on the catalysts surface is evaluated in terms of purity and thermal stability using Raman spectroscopy and TGA/DTG analysis respectively. The methane pyrolysis reaction is conducted in a temperature ranges (700–900 °C), and the activation energy has been calculated by Arrhenius equation to 17.7 kJ/mol (68.4 to 97.0 CH4 conv%). The SEM analysis shows random CNTs growth and due to nickel nanoparticles agglomeration at higher temperatures, the CNTs average diameters increases from 54 nm at 700 °C to 69 nm at 900 °C. The ID/IG ratio between 700 °C to 900 °C decreases from 0.86 to 0.53 which might occur by the oxidation of amorphous carbon at higher temperatures. The lower value of ID/IG at 900 °C can be explained by more formation of graphitized carbon and sp2 ordered structures at higher temperatures. Additionally, the XPS analysis shows a sharp peak of nickel metal (Ni0) in the Ni2p spectra of the used catalyst at 700 °C which guarantees high activity. The average diameter and length of synthesized CNTs at 700 °C are measured to be 54 nm and 0.8 μm, respectively, which leads to a unique aspect ratio (L/D) of 15. The average diameter and length of synthesized CNTs at 900 °C are measured to be 69 nm and 0.5 μm, respectively, which results in a unique aspect ratio of 7. The SEM on the used catalyst at 700 °C shows coil type of CNTs growth which is an ideal candidate for efficient electromagnetic absorbers and Li battery electrode. The tip-growth of CNTs on nickel catalyst is confirmed by SEM. Moreover, Ni3C peaks in XRD spectra at 700 and 750 °C show activity of nickel carbide in the methane pyrolysis reaction at lower temperatures. On the other hand, TGA analysis confirms the maximum carbon yield at 900 °C (12.19 gC/100 gCatalyst). The acid treatment increases the carbon yield to 75% at 750 °C. Finally, the kinetic study on the nickel catalyst used for microwave heating methane pyrolysis was carried out using the Arrhenius equation. The activation energy (17.7 kJ/mol) comparison with other related studies shows the efficiency of the nickel-based catalyst under microwave heating. The synthesized CNTs by microwave heating methane pyrolysis achieve high yield, coil type growth, and less defect carbon.
Turn-off fluorescent carbon quantum dot (CQD)-based sensors have emerged as highly efficient analytical platforms for tetracycline detection in complex food and environmental matrices. This review examines recent advances in CQD design and their fluorescence quenching behaviors, with a particular focus on mechanistic pathways governing signal attenuation. The dominant quenching processes, including the inner filter effect, static and dynamic quenching, charge transfer interactions, and aggregation-induced effects, are systematically analyzed in relation to their analytical implications. Special attention is given to how heteroatom doping, surface functionalization and structural heterogeneity influence emission properties and sensing performance. The review further evaluates the analytical performance of CQD-based turn-off systems in real sample environments such as milk, serum, honey, and environmental water, highlighting both their strengths in sensitivity and rapid response, as well as their limitations in terms of matrix interference, reproducibility, and calibration reliability. Despite significant progress, inconsistencies in mechanistic interpretation and lack of standardization remain key barriers to quantitative robustness. Overall, this work provides an integrated perspective on the relationship between photophysical quenching mechanisms and analytical outcomes, emphasizing the need for more rigorously designed CQD systems to achieve reliable tetracycline monitoring in complex matrices.
A sustainable strategy has been developed for the efficient removal of triiodide (I3–), a stable analogue of radioactive iodide, from aqueous solutions using activated carbons (ACs). The ACs were derived from agricultural biomass – specifically rice husk, buckwheat husk, and walnut shell. Carbonized biomass was impregnated with urea and subsequently activated with potassium hydroxide (KOH) to enrich the carbon surface with nitrogen-containing groups and achieve high surface area. The removal of I3– spiked in water was very effective with activated carbons with urea and KOH from rice husk and buckwheat husk, with uptakes up to 1212 ± 9 mg/g and 1099 ± 5 mg/g, respectively. These values almost tripled the uptake of I3– by a commercial granular activated carbon used in the water industry and also outperformed their respective physically and chemically (with KOH) AC carbons. The Langmuir and the Freundlich isotherm models provided insights into the adsorption mechanisms, confirming the beneficial effect of urea functionalization, particularly at equilibrium concentrations < 100 mg I3– /L. These findings highlight the advantages of using urea-impregnated biomass-derived ACs as cost-effective, scalable materials for the remediation of triiodide and, subsequently, the equivalent species from radioactive iodine in water treatment applications. Future work should address the scalability of the process to produce the best-performing ACs, as well as their performance in effluents from the nuclear industry.
In this study, activated carbon (AC) was produced from rice husks using a chemical activation method. The resulting material was characterized by a high specific surface area SBET = 2690 m²/g. To improve the adsorption properties, the activated carbon was further modified with MgAlFe-LDH layered double hydroxides to produce an AC/LDH composite. The textural and structural properties of the samples were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, Fourier-transform infrared spectroscopy and low-temperature nitrogen adsorption–desorption with the specific surface area calculated using the BET method. The resulting materials were investigated as adsorbents for the removal of Rose Bengal (RB) dye from aqueous solutions and real aqueous matrices. The influence of key process parameters, including contact time, solution pH, adsorbent dosage, and initial dye concentration, was studied. Under optimal adsorption conditions, a dye removal efficiency of 97.05% was achieved for AC and 100% for AC/LDH. Additionally, the possibility of reusing the adsorbents after regeneration was evaluated. Analysis of N₂ adsorption–desorption isotherms showed that the materials obtained possess a well-developed porous structure with the presence of micro- and mesopores. The equilibrium adsorption data were well described by the Langmuir model with high correlation coefficients for AC and AC/LDH - R² = 0.9969 and R² = 0.9966, respectively. The kinetic data were consistent with a pseudo-second-order model. Research has shown that the maximum adsorption capacity of AC and AC/LDH reached 544.56 and 508.5 mg/g respectively. Additional experiments under real-world conditions were conducted using wastewater from the Sorbulak storage tank. The results showed that the AC/LDH composite exhibits high adsorption efficiency in removing RB from real-world aqueous environments and demonstrates stability during reuse.
The energies of the highest occupied and lowest unoccupied molecular orbitals (HOMO, LUMO) and their difference, the HOMO–LUMO gap, are a recurring evaluation target in molecular machine learning. These quantities are sensitive to molecular conformation, their errors can compound when derived indirectly, and they connect to interpretable physical mechanisms (ionization potential, electron affinity, conjugation, reactivity). This combination makes them a uniquely demanding test for representation-learning architectures. Other QM9 targets, such as internal energy or heat capacity, are thermodynamic properties dominated by additive atomic contributions, so invariant models fit them comparatively easily. Frontier orbital energies are different: they depend on long-range electronic delocalization and orbital symmetry, properties that an architecture without explicit equivariance or higher-order interaction terms is not guaranteed to capture. This is why we treat HOMO/LUMO/gap prediction as a focused case study rather than folding it into a generic molecular-property survey. We conducted a systematic review following PRISMA 2020 guidelines, searching Google Scholar, Scopus, and IEEE Xplore with the reproducible query "QM9" AND ("HOMO" OR "LUMO") AND "transformer", then applied explicit inclusion and exclusion criteria requiring quantitative MAE reporting on QM9 (or its QM7/QM8 relatives) for at least one frontier-orbital-related target. The search returned 544 Google Scholar records, 11 Scopus records, and 0 eligible IEEE Xplore records (555 before deduplication). Of these, 59 studies published between 2022 and early 2026 met all criteria and were retained for structured data extraction across 49 fields covering architecture, physical priors, benchmark protocol, performance, computational cost, generalization, uncertainty quantification, and reproducibility. Pooled across the three target properties, equivariant architectures report a lower median MAE than invariant architectures (25.8 meV vs. 52.0 meV). However, the two distributions overlap substantially and both have long right tails, so we read this as a moderate, representation-dependent tendency rather than a categorical claim of superiority. Three-dimensional and hybrid 2D/3D representations dominate the corpus, reflecting a clear methodological preference, although 2D and 1D approaches remain competitive in a subset of studies; this comparison is also confounded by differences in pretraining data and model scale across studies. The corpus shows substantial, previously undocumented reporting gaps: algorithmic complexity is stated in only 35.6% of studies, inference latency in 23.7%, and predictive uncertainty in just 13.6%. We treat these gaps, rather than any single architectural finding, as the field’s most actionable shortcoming. We provide a consolidated comparative table classifying all 59 studies by representation, architecture family, physical prior, benchmark, and reported performance, and we discuss QM9’s known limitations (restriction to nine heavy atoms, closed-shell neutral species, single-conformer DFT geometries) as a boundary on how far every conclusion in this review can generalize.
Polymeric carbon precursors typically yield poorly ordered sp² hybridized carbon after pyrolysis at 1000 °C. This poor ordering results in a turbostratic carbon that lacks the thermomechanical properties of the carbon produced from more graphitizable precursors such as pitch. To overcome this limitation, we explored how incorporating metallocene additives from group 5 to group 10 (i.e., vanadocene, chromocene, manganocene, ferrocene, cobaltocene, nickelocene, and ruthenocene) influences the carbon crystallite thickness (LC) and thermal decomposition pathways of a non-graphitizable polybenzoxazine. The incorporation of these additives led to changes in the crystallinity of pyrolyzed carbon as well as the thermal decomposition behavior. Powder X-ray diffraction saw LC values ranging from 1.65 nm (ruthenocene) to 7.5 nm (nickelocene), with the higher LC values approaching graphitizable precursors (∼5 nm at 1000 °C) from metallocenes of groups 7–10. X-ray photoelectron spectroscopy was used to identify the chemical state of the metals post pyrolysis and provided an experimental check for the X-ray diffraction analysis. Additionally, low- and high-resolution TEM was used to investigate the carbon morphology, which provided visual context for the diffraction analysis. Thermogravimetric analysis of the decomposition of the polymer provided insight into the effects of metallocene additives during pyrolysis. Increases in mass yield were attributed to metallocene-catalyzed radical capture. Our results highlight the potential use of metallocene additives for controlling the crystallinity of carbon during pyrolysis.
There is a growing demand for selective and tuneable functionalisation of carbon materials through environmentally friendly and non-toxic processes. Here, we demonstrate that oxygen functional groups can be grafted onto graphite surfaces using arc plasma discharge at the liquid-gas interface, with a high degree of control over the functionalisation rate. Both the treatment duration and the reactor geometry significantly influence the surface chemistry, enabling oxygen contents ranging from 4 to 14 % (TGA-MS). Importantly, the plasma treatment introduces only a limited amount of structural defects, thereby preserving the intrinsic crystallinity and maintaining a good electrical conductivity of the functionalised graphite. Additionally, shock waves generated during the plasma discharge induce partial exfoliation of the graphite particles. This solvent-free process, relying solely on water and electricity, offers a low-energy and sustainable alternative to conventional oxidation methods. Overall, this work provides a scalable and controllable strategy for tailoring the surface chemistry of carbon materials, opening new opportunities for their integration into conductive polymer composites.
Most previously reported methods for creating interconnected carbon nanofiber (CNF) networks involve multiple complex steps and require precise control over various fabrication parameters. This study presents a straightforward and innovative approach to fabricate CNF architectures with tunable morphologies, ranging from isolated fibers to highly interconnected networks, porous and non-porous films by simply adjusting the heating rate during the stabilization of electrospun polyacrylonitrile (PAN). Leveraging the time-dependent thermochemical properties of PAN, the method eliminates the need for complex, multi-step processes. DSC analysis of PAN at varying heating rates led to this novel approach for controlling the morphology of electrospun CNF mats by balancing the rate of a physical phenomenon -melting- against a set of chemical reactions -stabilization-. By tuning the heating rate during the stabilization process at four levels of 0.25 degrees C/min, 0.5 degrees C/min, 1 degrees C/min and 2 degrees C/min, CNF mats with varying morphologies were obtained. Crystallographical methods represent a development in crystalline structure with an increase in in-plane crystalline size from 8.078 nm to 8.762 nm, an increase in number of stacking layers from 2.19 to 2.30 and a decrease in ID/IG from 2.380 to 2.195 when the morphology tends from separated fibers to interconnected networks. The resulting fully interconnected CNF mats exhibit a remarkable electrical conductivity of 62.81 S/cm, representing a significantly higher than 13.87 S/cm for isolated fibers, while maintaining similar chemical and structural properties across different morphologies. This work offers a controllable and efficient route to engineer CNF-based materials, highlighting their significant potential for advanced electrochemical applications.
